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06/15/2023
Retrospective: EIE: Efficient Inference Engine on Sparse and Compressed Neural Network
EIE proposed to accelerate pruned and compressed neural networks, exploi...
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03/05/2023
SatIn: Hardware for Boolean Satisfiability Inference
This paper describes SatIn, a hardware accelerator for determining boole...
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06/13/2022
Optimal Clipping and Magnitude-aware Differentiation for Improved Quantization-aware Training
Data clipping is crucial in reducing noise in quantization operations an...
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03/10/2021
PatchNet – Short-range Template Matching for Efficient Video Processing
Object recognition is a fundamental problem in many video processing tas...
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02/08/2021
VS-Quant: Per-vector Scaled Quantization for Accurate Low-Precision Neural Network Inference
Quantization enables efficient acceleration of deep neural networks by r...
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02/20/2020
SpArch: Efficient Architecture for Sparse Matrix Multiplication
Generalized Sparse Matrix-Matrix Multiplication (SpGEMM) is a ubiquitous...
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08/18/2019
A Delay Metric for Video Object Detection: What Average Precision Fails to Tell
Average precision (AP) is a widely used metric to evaluate detection acc...
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09/30/2018
CaTDet: Cascaded Tracked Detector for Efficient Object Detection from Video
Detecting objects in a video is a compute-intensive task. In this paper ...
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02/18/2018
Efficient Sparse-Winograd Convolutional Neural Networks
Convolutional Neural Networks (CNNs) are computationally intensive, whic...
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12/05/2017
Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training
Large-scale distributed training requires significant communication band...
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05/24/2017
Exploring the Regularity of Sparse Structure in Convolutional Neural Networks
Sparsity helps reduce the computational complexity of deep neural networ...
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05/23/2017
SCNN: An Accelerator for Compressed-sparse Convolutional Neural Networks
Convolutional Neural Networks (CNNs) have emerged as a fundamental techn...
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01/14/2017
HoLiSwap: Reducing Wire Energy in L1 Caches
This paper describes HoLiSwap a method to reduce L1 cache wire energy, a...
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12/01/2016
ESE: Efficient Speech Recognition Engine with Sparse LSTM on FPGA
Long Short-Term Memory (LSTM) is widely used in speech recognition. In o...
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07/15/2016
DSD: Dense-Sparse-Dense Training for Deep Neural Networks
Modern deep neural networks have a large number of parameters, making th...
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06/06/2016
CG-OoO: Energy-Efficient Coarse-Grain Out-of-Order Execution
We introduce the Coarse-Grain Out-of-Order (CG- OoO) general purpose pro...
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02/24/2016
SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size
Recent research on deep neural networks has focused primarily on improvi...
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02/04/2016
EIE: Efficient Inference Engine on Compressed Deep Neural Network
State-of-the-art deep neural networks (DNNs) have hundreds of millions o...
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10/01/2015
Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding
Neural networks are both computationally intensive and memory intensive,...
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06/08/2015